Large models, encompassing large language and diffusion models, have shown
exceptional promise in approximating human-level intelligence, garnering significant
interest from both academic and industrial spheres. However, the training of these large
models necessitates vast quantities of high-quality data, and with continuous updates to
these models, the existing reservoir of high-quality data may soon be depleted. This
challenge has catalyzed a surge in research focused on data augmentation methods …